Search Results for author:
Found 9 papers, 3 papers with code
Zero-shot Generative Linguistic Steganography
Generative linguistic steganography attempts to hide secret messages into covertext.
Lost in Overlap: Exploring Watermark Collision in LLMs
The proliferation of large language models (LLMs) in generating content raises concerns about text copyright.
PISA: Point-cloud-based Instructed Scene Augmentation
Indoor scene augmentation has become an emerging topic in the field of computer vision with applications in augmented and virtual reality.
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor
To increase the correctness of the evaluation of architectures, besides direct evaluation using the inherited weights, we further apply a few-shot predictor to assess the architecture on the other hand.
Semi-Supervised Learning for Video Captioning
In the proposed study, we make the first attempt to train the video captioning model on labeled data and unlabeled data jointly, in a semi-supervised learning manner.
Optimally Combining Classifiers for Semi-Supervised Learning
In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine.
Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020
This report describes our model for VATEX Captioning Challenge 2020.
A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling
Given the features of a video, recurrent neural networks can be used to automatically generate a caption for the video.
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors
We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics.
